class_docstr: Abstract interface for collection of tracked series/sequences.
  
  Typically represents sequences of a same run or sequences matching given query expression.
code: def dataframe(
      self,
      only_last: bool = False,
      include_run=True,
      include_name=True,
      include_context=True,
      include_props=True,
      include_params=True,
  ) -> "DataFrame":
      # TODO [GA]: Separate runs and sequences dataframes collection
      dfs = []
      if self._item == "run":
          dfs = [
              run.run.dataframe(
                  include_props=include_props,
                  include_params=include_params,
              )
              for run in self.iter_runs()
          ]
      elif self._item == "sequence":
          dfs = [
              metric.dataframe(
                  include_run=include_run,
                  include_name=include_name,
                  include_context=include_context,
                  only_last=only_last,
              )
              for metric in self
          ]
      if not dfs:
          return None
      import pandas as pd
  
      return pd.concat(dfs)
  
imports: import logging
  from abc import abstractmethod
  from typing import Iterator
  from typing import TYPE_CHECKING
  from tqdm import tqdm
  from aim.sdk.sequence import Sequence
  from aim.sdk.types import QueryReportMode
  from aim.sdk.query_utils import RunView, SequenceView
  from aim.storage.query import RestrictedPythonQuery
label_desc: Use image node_id 1 for calling the SequenceCollection obj's underlying member method code with example usage: obj.dataframe(only_last, include_run, include_name, include_context, include_props, include_params) and returns: pd, None